125 resultados para Conveying machinery
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Direct contact mechanism in bioleaching implies prior mineral adhesion of Acidithiobacillus ferrooxidans and subsequent enzymatic attack.Prior bacterial adaptation to sulfide mineral substrates influences bacterial ferrous ion oxidation rates. It is highly beneficial to understand major biooxidation mechanisms with reference to solution- and mineral-grown cells in order to optimize bioleaching reactions. For A. ferrooxidans grown in the presence of solid substrates such as sulfur, pyrite and chalcopyrite, bacterial adhesion is required for its enzymatic machinery to come into close contact for mineral dissolution.But when grown in solution substrate such as ferrous ions and thiosulfate, such an adhesion machinery is not required for substrate utilization. Proteinaceous compounds were observed on the surface of sulfur-grown cells. Such an induction of relatively hydrophobic proteins and down regulation of exposed polysaccharides leads to changes in cell surface chemistry. Sulfur-grown and pyrite- and chalcopyrite-grown bacterial cells were found to be more efficient in the bioleaching of chalcopyrite than those grown in the presence of ferrous ions and thiosulfate. (C) 2010 Elsevier B.V. All rights reserved.
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We propose a novel second order cone programming formulation for designing robust classifiers which can handle uncertainty in observations. Similar formulations are also derived for designing regression functions which are robust to uncertainties in the regression setting. The proposed formulations are independent of the underlying distribution, requiring only the existence of second order moments. These formulations are then specialized to the case of missing values in observations for both classification and regression problems. Experiments show that the proposed formulations outperform imputation.
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The Reeb graph of a scalar function represents the evolution of the topology of its level sets. In this video, we describe a near-optimal output-sensitive algorithm for computing the Reeb graph of scalar functions defined over manifolds. Key to the simplicity and efficiency of the algorithm is an alternate definition of the Reeb graph that considers equivalence classes of level sets instead of individual level sets. The algorithm works in two steps. The first step locates all critical points of the function in the domain. Arcs in the Reeb graph are computed in the second step using a simple search procedure that works on a small subset of the domain that corresponds to a pair of critical points. The algorithm is also able to handle non-manifold domains.
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We study wireless multihop energy harvesting sensor networks employed for random field estimation. The sensors sense the random field and generate data that is to be sent to a fusion node for estimation. Each sensor has an energy harvesting source and can operate in two modes: Wake and Sleep. We consider the problem of obtaining jointly optimal power control, routing and scheduling policies that ensure a fair utilization of network resources. This problem has a high computational complexity. Therefore, we develop a computationally efficient suboptimal approach to obtain good solutions to this problem. We study the optimal solution and performance of the suboptimal approach through some numerical examples.
Analyzing Cache Performance Bottlenecks of STM Applications and addressing them with Compiler's help
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Software transactional memory (STM) is a promising programming paradigm for shared memory multithreaded programs as an alternative to traditional lock based synchronization. However adoption of STM in mainstream software has been quite low due to its considerable overheads and its poor cache/memory performance. In this paper, we perform a detailed study of the cache behavior of STM applications and quantify the impact of different STM factors on the cache misses experienced by the applications. Based on our analysis, we propose a compiler driven Lock-Data Colocation (LDC), targeted at reducing the cache overheads on STM. We show that LDC is effective in improving the cache behavior of STM applications by reducing the dcache miss latency and improving execution time performance.
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In this work, we propose a new organization for the last level shared cache of a rnulticore system. Our design is based on the observation that the Next-Use distance, measured in terms of intervening misses between the eviction of a line and its next use, for lines brought in by a given delinquent PC falls within a predictable range of values. We exploit this correlation to improve the performance of shared caches in multi-core architectures by proposing the NUcache organization.
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Heat shock protein 90 participates in diverse biological processes ranging from protein folding, cell cycle, signal transduction and development to evolution in all eukaryotes. It is also critically involved in regulating growth of protozoa such as Dictyostelium discoideum, Leishmania donovani, Plasmodium falciparum, Trypanosoma cruzi, and Trypanosoma evansi. Selective inhibition of Hsp90 has also been explored as an intervention strategy against important human diseases such as cancer, malaria, or trypanosomiasis. Giardia lamblia, a simple protozoan parasite of humans and animals, is an important cause of diarrheal disease with significant morbidity and some mortality in tropical countries. Here we show that the G. lamblia cytosolic hsp90 ( glhsp90) is split in two similar sized fragments located 777 kb apart on the same scaffold. Intrigued by this unique arrangement, which appears to be specific for the Giardiinae, we have investigated the biosynthesis of GlHsp90. We used genome sequencing to confirm the split nature of the giardial hsp90. However, a specific antibody raised against the peptide detected a product with a mass of about 80 kDa, suggesting a post-transcriptional rescue of the genomic defect. We show evidence for the joining of the two independent Hsp90 transcripts in-trans to one long mature mRNA presumably by RNA splicing. The splicing junction carries hallmarks of classical cis-spliced introns, suggesting that the regular cis-splicing machinery may be sufficient for repair of the open reading frame. A complementary 26-nt sequence in the ``intron'' regions adjacent to the splice sites may assist in positioning the two pre-mRNAs for processing. This is the first example of post-transcriptional rescue of a split gene by trans-splicing.
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Design, fabrication and preliminary testing of a flat pump with millimetre thickness are described in this paper. The pump is entirely made of polymer materials barring the magnet and copper coils used for electromagnetic actuation. The fabrication is carried out using widely available microelectronic packaging machinery and techniques. Therefore, the fabrication of the pump is straightforward and inexpensive. Two types of prototypes are designed and built. One consists of copper coils that are etched on an epoxy plate and the other has wound insulated wire of 90 mu m diameter to serve as a coil. The overall size of the first pump is 25 mm x 25 mm x 3.6 mm including the 3.1 mm-thick NdFeB magnet of diameter 12 mm. It consists of a pump chamber of 20 mm x 20 mm x 0.8 mm with copper coils etched from a copper-clad epoxy plate using dry-film lithography and milled using a CNC milling machine, two passive valves and the pump-diaphragm made of Kapton film of 0.089 mm thickness. The second pump has an overall size of 35 mm x 35 mm x 4.4 mm including the magnet and the windings. A breadboard circuit and DC power supply are used to test the pump by applying an alternating square-wave voltage pulse. A water slug in a tube attached to the inlet is used to observe and measure the air-flow induced by the pump against atmospheric pressure. The maximum flow rate was found to be 15 ml/min for a voltage of 2.5 V and a current of 19 mA at 68 Hz.
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We develop four algorithms for simulation-based optimization under multiple inequality constraints. Both the cost and the constraint functions are considered to be long-run averages of certain state-dependent single-stage functions. We pose the problem in the simulation optimization framework by using the Lagrange multiplier method. Two of our algorithms estimate only the gradient of the Lagrangian, while the other two estimate both the gradient and the Hessian of it. In the process, we also develop various new estimators for the gradient and Hessian. All our algorithms use two simulations each. Two of these algorithms are based on the smoothed functional (SF) technique, while the other two are based on the simultaneous perturbation stochastic approximation (SPSA) method. We prove the convergence of our algorithms and show numerical experiments on a setting involving an open Jackson network. The Newton-based SF algorithm is seen to show the best overall performance.
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An (alpha, beta)-spanner of an unweighted graph G is a subgraph H that distorts distances in G up to a multiplicative factor of a and an additive term beta. It is well known that any graph contains a (multiplicative) (2k - 1, 0)-spanner of size O(n(1+1/k)) and an (additive) (1, 2)-spanner of size O(n(3/2)). However no other additive spanners are known to exist. In this article we develop a couple of new techniques for constructing (alpha, beta)-spanners. Our first result is an additive (1, 6)-spanner of size O(n(4/3)). The construction algorithm can be understood as an economical agent that assigns costs and values to paths in the graph, purchasing affordable paths and ignoring expensive ones, which are intuitively well approximated by paths already purchased. We show that this path buying algorithm can be parameterized in different ways to yield other sparseness-distortion tradeoffs. Our second result addresses the problem of which (alpha, beta)-spanners can be computed efficiently, ideally in linear time. We show that, for any k, a (k, k - 1)-spanner with size O(kn(1+1/k)) can be found in linear time, and, further, that in a distributed network the algorithm terminates in a constant number of rounds. Previous spanner constructions with similar performance had roughly twice the multiplicative distortion.
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We review the current status of various aspects of biopolymer translocation through nanopores and the challenges and opportunities it offers. Much of the interest generated by nanopores arises from their potential application to third-generation cheap and fast genome sequencing. Although the ultimate goal of single-nucleotide identification has not yet been reached, great advances have been made both from a fundamental and an applied point of view, particularly in controlling the translocation time, fabricating various kinds of synthetic pores or genetically engineering protein nanopores with tailored properties, and in devising methods (used separately or in combination) aimed at discriminating nucleotides based either on ionic or transverse electron currents, optical readout signatures, or on the capabilities of the cellular machinery. Recently, exciting new applications have emerged, for the detection of specific proteins and toxins (stochastic biosensors), and for the study of protein folding pathways and binding constants of protein-protein and protein-DNA complexes. The combined use of nanopores and advanced micromanipulation techniques involving optical/magnetic tweezers with high spatial resolution offers unique opportunities for improving the basic understanding of the physical behavior of biomolecules in confined geometries, with implications for the control of crucial biological processes such as protein import and protein denaturation. We highlight the key works in these areas along with future prospects. Finally, we review theoretical and simulation studies aimed at improving fundamental understanding of the complex microscopic mechanisms involved in the translocation process. Such understanding is a pre-requisite to fruitful application of nanopore technology in high-throughput devices for molecular biomedical diagnostics.
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SecB is a homotetrameric cytosolic chaperone that forms part of the protein translocation machinery in E. coli. Due to SecB, nascent polypeptides are maintained in an unfolded translocation-competent state devoid of tertiary structure and thus are guided to the translocon. In vitro SecB rapidly binds to a variety of ligands in a non-native state. We have previously investigated the bound state conformation of the model substrate bovine pancreatic trypsin inhibitor (BPTI) as well as the conformation of SecB itself by using proximity relationships based on site-directed spin labeling and pyrene fluorescence methods. It was shown that SecB undergoes a conformational change during the process of substrate binding. Here, we generated SecB mutants containing but a single cysteine per subunit or an exposed highly reactive new cysteine after removal of the nearby intrinsic cysteines. Quantitative spin labeling was achieved with the methanethiosulfonate spin label (MTS) at positions C97 or E90C, respectively. Highfield (W-band) electron paramagnetic resonance (EPR) measurements revealed that with BPTI present the spin labels are exposed to a more polar/hydrophilic environment. Nanoscale distance measurements with double electron-electron resonance (DEER) were in excellent agreement with distances obtained by molecular modeling. Binding of BPTI also led to a slight change in distances between labels at C97 but not at E90C. While the shorter distance in the tetramer increased, the larger diagonal distance decreased. These findings can be explained by a widening of the tetrameric structure upon substrate binding much like the opening of two pairs of scissors.
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MATLAB is an array language, initially popular for rapid prototyping, but is now being increasingly used to develop production code for numerical and scientific applications. Typical MATLAB programs have abundant data parallelism. These programs also have control flow dominated scalar regions that have an impact on the program's execution time. Today's computer systems have tremendous computing power in the form of traditional CPU cores and throughput oriented accelerators such as graphics processing units(GPUs). Thus, an approach that maps the control flow dominated regions to the CPU and the data parallel regions to the GPU can significantly improve program performance. In this paper, we present the design and implementation of MEGHA, a compiler that automatically compiles MATLAB programs to enable synergistic execution on heterogeneous processors. Our solution is fully automated and does not require programmer input for identifying data parallel regions. We propose a set of compiler optimizations tailored for MATLAB. Our compiler identifies data parallel regions of the program and composes them into kernels. The problem of combining statements into kernels is formulated as a constrained graph clustering problem. Heuristics are presented to map identified kernels to either the CPU or GPU so that kernel execution on the CPU and the GPU happens synergistically and the amount of data transfer needed is minimized. In order to ensure required data movement for dependencies across basic blocks, we propose a data flow analysis and edge splitting strategy. Thus our compiler automatically handles composition of kernels, mapping of kernels to CPU and GPU, scheduling and insertion of required data transfer. The proposed compiler was implemented and experimental evaluation using a set of MATLAB benchmarks shows that our approach achieves a geometric mean speedup of 19.8X for data parallel benchmarks over native execution of MATLAB.
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Context-sensitive points-to analysis is critical for several program optimizations. However, as the number of contexts grows exponentially, storage requirements for the analysis increase tremendously for large programs, making the analysis non-scalable. We propose a scalable flow-insensitive context-sensitive inclusion-based points-to analysis that uses a specially designed multi-dimensional bloom filter to store the points-to information. Two key observations motivate our proposal: (i) points-to information (between pointer-object and between pointer-pointer) is sparse, and (ii) moving from an exact to an approximate representation of points-to information only leads to reduced precision without affecting correctness of the (may-points-to) analysis. By using an approximate representation a multi-dimensional bloom filter can significantly reduce the memory requirements with a probabilistic bound on loss in precision. Experimental evaluation on SPEC 2000 benchmarks and two large open source programs reveals that with an average storage requirement of 4MB, our approach achieves almost the same precision (98.6%) as the exact implementation. By increasing the average memory to 27MB, it achieves precision upto 99.7% for these benchmarks. Using Mod/Ref analysis as the client, we find that the client analysis is not affected that often even when there is some loss of precision in the points-to representation. We find that the NoModRef percentage is within 2% of the exact analysis while requiring 4MB (maximum 15MB) memory and less than 4 minutes on average for the points-to analysis. Another major advantage of our technique is that it allows to trade off precision for memory usage of the analysis.